Description Usage Arguments Details Value Note Author(s) References Examples
Applies the UfsCov algorithm based on the space filling concept, by using a sequatial forward search (SFS).
1 |
data |
Data of class: |
Since the algorithm is based on pairwise distances, and
according to the computing power of your machine, large number of
data points can take much time and needs more memory.
See UfsCov_par
for parellel computing, or
UfsCov_ff
for memory efficient storage of large data
on disk and fast access (by using the ff
and the ffbase
packages).
A list of two elements:
CovD
a vector containing the coverage measure of
each step of the SFS.
IdR
a vector containing the added variables during
the selection procedure.
The algorithm does not deal with missing values and constant features. Please make sure to remove them.
Mohamed Laib Mohamed.Laib@unil.ch
M. Laib and M. Kanevski (2017). Unsupervised Feature Selection Based on Space Filling Concept, arXiv:1706.08894.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | infinity<-Infinity(n=800)
Results<- UfsCov(infinity)
cou<-colnames(infinity)
nom<-cou[Results[[2]]]
par(mfrow=c(1,1), mar=c(5,5,2,2))
names(Results[[1]])<-cou[Results[[2]]]
plot(Results[[1]] ,pch=16,cex=1,col="blue", axes = FALSE,
xlab = "Added Features", ylab = "Coverage measure")
lines(Results[[1]] ,cex=2,col="blue")
grid(lwd=1.5,col="gray" )
box()
axis(2)
axis(1,1:length(nom),nom)
which.min(Results[[1]])
## Not run:
#### UfsCov on the Butterfly dataset ####
require(IDmining)
N <- 1000
raw_dat <- Butterfly(N)
dat<-raw_dat[,-9]
Results<- UfsCov(dat)
cou<-colnames(dat)
nom<-cou[Results[[2]]]
par(mfrow=c(1,1), mar=c(5,5,2,2))
names(Results[[1]])<-cou[Results[[2]]]
plot(Results[[1]] ,pch=16,cex=1,col="blue", axes = FALSE,
xlab = "Added Features", ylab = "Coverage measure")
lines(Results[[1]] ,cex=2,col="blue")
grid(lwd=1.5,col="gray" )
box()
axis(2)
axis(1,1:length(nom),nom)
which.min(Results[[1]])
## End(Not run)
|
================================================================================x2
2
Loading required package: IDmining
================================================================================X1
3
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.